This survey introduces a three-layer taxonomy (Signal, Physical, and Semantic) to systematically organize research advances in polarimetric forward imaging and identifies persistent challenges, notably the scarcity of standardized datasets and the structural limitations of standard network architectures in handling highly sensitive physical signals.
This work proposes a model-driven deep neural network to effectively handle the joint degradation of low light and blur and designs an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise.
Yao Xiao, You-Shen Xia, Zhen-Yu Lu et al.· IEEE Transactions on Neural...· 0 citations
Polarimetric vision is gaining increasing attention because it provides physical cues about scene shape, material, and reflection that are difficult to recover from RGB alone. Recent work has therefore explored predicting polarization directly from conventional RGB images; however, the fidelity of these methods strongl...
Param Sangani, Ahmad Moori, Erik Blasch et al.· 0 citations
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real-world applications. A key challenge...
Zhi-Hua Wang, Qing-Hua Lin, Fei-Yang Liu et al.· IEEE Transactions on Image P...· 0 citations
Most deep-learning-based panchromatic (PAN)-sharpening methods are trained under simplified and fixed degradation models, typically assuming an isotropic Gaussian point spread function (PSF). However, real electro-optical (EO) satellite systems exhibit scene- and acquisition-dependent blur characteristics caused by dif...
Hyun-Ho Kim, Munchurl Kim· IEEE Transactions on Geoscie...· 0 citations
Division-of-focal-plane polarimeter (DoFP) cameras enable real-time polarization imaging by simultaneously capturing multiple polarization orientations at the cost of spatial resolution. Existing conventional and deep learning-based reconstruction methods still struggle to robustly estimate Degree of Polarization (DoP)...
Yi-Dong Luo, Cai-Yun Wu, Cheng-Gong Li et al.· IEEE Transactions on Image P...· 0 citations
This paper develops a Retinex-guided state space backbone that integrates illumination–reflectance priors into the network in a structured manner, enabling more reliable restoration of shadow regions, and introduces a Shadow Interaction Module within a multi-scale channel attention framework.
Wei-Ju Zhao, Run-Xing Zhao, Shi-Jian Yang· Signal, Image and Video Proc...· 0 citations
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